Combinatorial multimer staining and spectral flow cytometry facilitate quantification and characterization of polysaccharide-specific B cell immunity

Bacterial capsular polysaccharides are important vaccine immunogens. However, the study of polysaccharide-specific immune responses has been hindered by technical restrictions. Here, we developed and validated a high-throughput method to analyse antigen-specific B cells using combinatorial staining with fluorescently-labelled capsular polysaccharide multimers. Concurrent staining of 25 cellular markers further enables the in-depth characterization of polysaccharide-specific cells. We used this assay to simultaneously analyse 14 Streptococcus pneumoniae or 5 Streptococcus agalactiae serotype-specific B cell populations. The phenotype of polysaccharide-specific B cells was associated with serotype specificity, vaccination history and donor population. For example, we observed a link between non-class switched (IgM+) memory B cells and vaccine-inefficient S. pneumoniae serotypes 1 and 3. Moreover, B cells had increased activation in donors from South Africa, which has high-incidence of S. agalactiae invasive disease, compared to Dutch donors. This assay allows for the characterization of heterogeneity in B cell immunity that may underlie immunization efficacy.


Supplementary Fig. 2 .
Validation of PBMCs probing with PS-SA multimers.a, Frequency of PSspecific cells in PBMCs from 3 donors with assumed no PCV13 vaccination, stained with 5μM (light grey) or 9μM (dark grey) PS-SA multimers for 14 serotypes.b, Correlation plot showing similarity between the staining of PBMCs with different concentrations in (a).Statistical analysis by Pearson correlation.c-d, example flow cytometry density plots (c) and frequencies (d) of PS-specific cells.Three donors (scales of grey) were stained each with three different SA-fluorochrome combinations (circle BV421/BUV615, triangle BV711/BUV661 and square BV785/BB515) for PS7F, PS19F and PS15B.Cells were pre-gated on live singlet SSC low CD7 -CD19 + cells.e, Percentage of cross-reactive cells among all PS-positive in all donors from Fig. 3e.f, Correlation between two experimental duplicates of stained PBMCs from one donor 3 weeks post-PCV13.The black lines indicates linear regression (R 2 =0.9614) and the 95% confidence interval.Statistical analysis by Pearson correlation.g, Frequency of cells in the "empty" category (BUV615 + BV711 + ) from all donors from (Fig. 3) to indicate the background and lower limit of detection.Average, the standard deviation and individual results are shown.Supplementary Fig. 3. Marker expression distribution across the UMAP.a, Density plots from all clustered B cells from (Fig. 4).b, Clusters of IgD + IgM + (orange), IgD + (dark green), IgM + (light green), IgA + (blue) and IgG + (magenta) gated B cells on a UMAP, from (Fig. 4a-b).Supplementary Fig. 4. Optimization of GBS PS biotinylation and PS-multimer formation.a, Bar plots with biotin ELISA results showing the efficacy of biotin incorporation in CDAP-activated GBS PS (light grey) and pneumococcal PS6B as positive control (dark grey).Untreated PS are shown as negative control (black).Blank-corrected OD values are shown.For each PS, levels were measured in duplicate, indicated by symbols with average depicted by bars.b, Competition ELISA showing the percentage of antibody blocking by biotinylated and non-modified PS.For each PS, inhibition of detection by pre-incubation of pooled donor samples (confirmed presence of serotype-specific antibodies), with autologous unmodified PS (aPS), autologous biotinylated PS (aPSbio), heterologous unmodified PS (hetPS) is shown.Inhibition indicates the reduction in OD signal in pre-absorbed versus non-pre-absorbed serum.Average of duplicates is shown.c, Competition ELISA as (b), showing all autologous and heterologous combinations of non-biotinylated PS, to show cross-reactivity of serotype-specific antibodies.Average of duplicates is shown.d, Histograms showing fluorescent signal of PS-SA multimers on compensation beads alone (Neg; black), with PS-SA-multimers (PS-; grey) or beads coupled with PS-specific autologous antiserum and stained with PS-SA-multimers (PS+; red), normalised to mode.e, Histograms showing signal of compensation beads with PS-SAmultimers alone without antiserum to indicate background (PS-; black) or compensation beads coupled to autologous serotype-specific antiserum and stained with autologous (aPS+; magenta) or heterologous (hPS+; light blue are combined heterologous PS, dark blue are heterologous PSIa/PSIb) PS-SA-multimers, normalised to mode.f, Frequency of PSIb and PSIII-specific cells in pooled PBMCs from 6 South African donors, after staining for all 5 serotypes (PSIa, PSIb, PSII, PSIII and PSV) or solely 2 (PSIb and PSIII).g, Barcode method of combinatorial staining patterns for GBS PS-SA multimers.The four fluorochromes are indicated in rows and the various serotypes in columns, including an empty colour combination for background control.h, Staining of pooled PBMCs of South African donors, with viability dye, CD3, CD19 and 3 concentrations of PS-SA-multimers: 1μg/mL, 2.5μg/mL and 5μg/mL.The fluorescence minus multimer (FMM) control sample was made by pooling all samples and staining with everything but multimers.Supplementary Fig. 5. Verification on GBS PS-multimer staining.a, Gating strategy for Dutch and South African PBMCs stained with the panel in (Supplementary table

i,
Example flow cytometry density plots from 3 donors from Figure 5, showing signal intensity of IgD and IgM (top panels) on pre-gated B cells (a) and IgG and IgA (bottom panels) on IgDIgM-negative cells.j, Arcsine transformed mean fluorescence intensity (MFI) of IgD, IgM, IgG and IgA-isotypes in each cluster from Fig. 5e-f.k, Heatmap showing the difference in confidence intervals for each serotype and isotype between Dutch and South African (SA) donors, assessed through a two-tailed test based on a linear regression model with wild bootstrap simulation at 9999 resamples.Bonferroni-Hochberg correction for multiple testing was employed.Stars indicate which PS-specific B cells and isotype are significantly associated to South African donors.(* p<0.05)Supplementary Fig. 6.Marker expression distribution across the UMAP of GBS samples.

table 2 Supplementary table 2. Statistical results from linear regression model bootstrapping for Spn cross-sectional phenotype and vaccination analysis. The
observed (obs_estimate) and estimated (unbiased_estimate) difference in proportion of clusters among total B cells comparing PCV13-vaccinated (M4) to non-vaccinated individuals were generated with an upper-sided linear regression model with wild bootstrap simulation (9999 resamples).Confidence intervals (ci) and Bonferroni's method adjusted ci (ci.adj) are shown, as well as statistical significance (signif_ci/ signif_ci.adj= 1).

table 4 Supplementary table 4. Statistical results from linear regression model bootstrapping for GBS isotype and population analysis.
Tableshowsthe observed (obs_estimate) and estimated (unbiased_estimate) difference in proportions of clusters among total B cells for a given serotype-specificity and B cell isotype, comparing South African-with Dutch donors.The analysis was performed using a two-sided linear regression model with wild bootstrap simulation (9999 resamples).Confidence intervals (ci) and Bonferroni's method adjusted ci (ci.adj) are shown, as well as statistical significance (signif_ci/ signif_ci.adj= 1).

table 5. Statistical results from linear regression model bootstrapping for GBS phenotype and population analysis.
The observed (obs_estimate) and estimated (unbiased_estimate) difference in proportions of clusters among total B cells for a given serotype-specificity and donor population (South African-over Dutch donors) were generated with a two-sided linear regression model with wild bootstrap simulation (9999 resamples).Confidence intervals (ci) and Bonferroni's method adjusted ci (ci.adj) are shown, as well as statistical significance (signif_ci/ signif_ci.adj= 1).